Translating cellular aging clocks into disease risk prediction.

Authors

Shimaa Heikal, Mohamed Salama

Published in

Cell reports. Medicine. Volume 7. Issue 8. Pages 102996. Aug 18, 2026.

Abstract

Ding et al. mapped over 7,000 plasma proteins to more than 40 cell types and developed machine learning aging clocks across 60,000 individuals, demonstrating that cell-type-specific biological aging is heterogeneous, measurable from blood alone, and powerfully predictive of neurodegenerative disease, cancer, and mortality up to 15 years before clinical onset.¹.

PMID:
42612620
Bibliographic data and abstract were imported from PubMed on 19 Aug 2026.

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